{"id":12038,"date":"2023-07-06T15:13:58","date_gmt":"2023-07-06T13:13:58","guid":{"rendered":"https:\/\/new.sano.science\/?post_type=people&p=12038"},"modified":"2026-02-17T11:45:11","modified_gmt":"2026-02-17T10:45:11","slug":"monika-pytlarz","status":"publish","type":"people","link":"https:\/\/sano.science\/people\/monika-pytlarz\/","title":{"rendered":"Monika Pytlarz"},"excerpt":{"rendered":"

PhD Student in Computational Neuroscience<\/p>\n","protected":false},"featured_media":28733,"template":"","people_teams":[19,33],"class_list":["post-12038","people","type-people","status-publish","has-post-thumbnail","hentry","people_teams-research","people_teams-computational-neuroscience"],"yoast_head":"\nMonika Pytlarz - Centre for Computational Personalized Medicine<\/title>\n<meta name=\"description\" content=\"Monika Pytlarz PhD Student in Computational Neuroscience.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/sano.science\/people\/monika-pytlarz\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Monika Pytlarz\" \/>\n<meta property=\"og:description\" content=\"Monika Pytlarz PhD Student in Computational Neuroscience.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/sano.science\/people\/monika-pytlarz\/\" \/>\n<meta property=\"og:site_name\" content=\"Centre for Computational Personalized Medicine\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/sano.science\/\" \/>\n<meta property=\"article:modified_time\" content=\"2026-02-17T10:45:11+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/sano.science\/wp-content\/uploads\/2023\/07\/Monika-Pytlarz.png\" \/>\n\t<meta property=\"og:image:width\" content=\"650\" \/>\n\t<meta property=\"og:image:height\" content=\"650\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:site\" content=\"@sanoscience\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"1 minute\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/sano.science\\\/people\\\/monika-pytlarz\\\/\",\"url\":\"https:\\\/\\\/sano.science\\\/people\\\/monika-pytlarz\\\/\",\"name\":\"Monika Pytlarz - 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Her research combines radiology, histopathology, and genomics to improve tumor classification and patient risk stratification, despite the biological heterogeneity of gliomas. The project’s computational image analysis of immunostaining provides insights into the tumor immune microenvironment, supporting the development of more precise therapeutic strategies. She also investigates cross-modal image translation, including the synthesis of histology from MRI scans.<\/p>\n<p>Monika obtained a BSc in Electroradiology from Collegium Medicum of the Jagiellonian University and an MSc in Bioinformatics from JU, and brings clinical experience from hospital diagnostic imaging departments. She explores translating AI models into clinical practice via computer-aided diagnosis tools and PACS integration.<\/p>\n<p> <\/p>\n<ul>\n<li><a href=\"https:\/\/www.rsipvision.com\/ComputerVisionNews-2024July\/40\/\">rsipvision.com\/ComputerVisionNews-2024July\/40\/<\/a><\/li>\n<\/ul>\n","email":"","social_media":[{"icon":{"ID":11994,"id":11994,"title":"linkedin","filename":"linkedin.svg","filesize":914,"url":"https:\/\/sano.science\/wp-content\/uploads\/2023\/05\/linkedin.svg","link":"https:\/\/sano.science\/people\/maciej-malawski\/linkedin-2\/","alt":"","author":"5","description":"","caption":"","name":"linkedin-2","status":"inherit","uploaded_to":531,"date":"2023-07-06 11:24:13","modified":"2023-07-06 11:24:13","menu_order":0,"mime_type":"image\/svg+xml","type":"image","subtype":"svg+xml","icon":"https:\/\/sano.science\/wp-includes\/images\/media\/default.png","width":1,"height":1,"sizes":{"thumbnail":"https:\/\/sano.science\/wp-content\/uploads\/2023\/05\/linkedin.svg","thumbnail-width":150,"thumbnail-height":150,"medium":"https:\/\/sano.science\/wp-content\/uploads\/2023\/05\/linkedin.svg","medium-width":300,"medium-height":300,"medium_large":"https:\/\/sano.science\/wp-content\/uploads\/2023\/05\/linkedin.svg","medium_large-width":768,"medium_large-height":1,"large":"https:\/\/sano.science\/wp-content\/uploads\/2023\/05\/linkedin.svg","large-width":1024,"large-height":1024,"1536x1536":"https:\/\/sano.science\/wp-content\/uploads\/2023\/05\/linkedin.svg","1536x1536-width":1,"1536x1536-height":1,"2048x2048":"https:\/\/sano.science\/wp-content\/uploads\/2023\/05\/linkedin.svg","2048x2048-width":1,"2048x2048-height":1}},"link":"https:\/\/www.linkedin.com\/in\/mnk-pltrz\/","name":"LinkedIn"}],"tabs":false,"quote":"","position_with_team":{"text_before_link":"PhD Student in","link_text":"Computational Neuroscience","text_after_link":"","link":"https:\/\/sano.science\/research-teams\/computer-vision-brain-and-more-lab\/"},"publications":[{"ID":21526,"post_author":"8","post_date":"2025-02-17 18:25:57","post_date_gmt":"2025-02-17 17:25:57","post_content":"<!-- wp:heading {\"epAnimationGeneratedClass\":\"edplus_anim-aKHf29\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h2 class=\"wp-block-heading eplus-wrapper\" id=\"h-szymon-mazurek-nbsp-monika-pytlarz-nbsp-sylwia-malec-alessandro-crimi-nbsp\">Szymon Mazurek,  Monika Pytlarz,  Sylwia Malec, Alessandro Crimi <\/h2>\n<!-- \/wp:heading -->\n\n<!-- wp:spacer {\"height\":\"30px\",\"epAnimationGeneratedClass\":\"edplus_anim-vfGiFQ\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-kaIHoN\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">Advancements across various industries have been significantly propelled by artificial intelligence. However, the rapid proliferation of these technologies also raises environmental concerns, particularly due to the substantial carbon footprints associated with training computational models. Segmenting the fetal brain in medical imaging presents a challenge due to its small size and the limited quality of fast 2D sequences. Deep neural networks emerge as a promising solution to this issue. The development of larger models in this context requires significant data and computing resources, leading to increased energy consumption. Our research focuses on exploring model architectures and compression techniques that enhance energy efficiency. We aim to optimize the balance between accuracy and energy usage through strategies such as designing lightweight networks, conducting architecture searches, and utilizing optimized distributed training tools. We have identified several effective strategies, including optimizing data loading, employing modern optimizers, implementing distributed training strategies, and reducing the precision of floating-point operations in light model architectures while adjusting parameters to match available computing resources. Our findings confirm that these methods ensure satisfactory model performance with minimal energy consumption during the training of deep neural networks for medical image segmentation.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"30px\",\"epAnimationGeneratedClass\":\"edplus_anim-vfGiFQ\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-aRLcXy\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"><strong>DOI<\/strong>: 10.1007\/978-3-031-63772-8_5<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"30px\",\"epAnimationGeneratedClass\":\"edplus_anim-vfGiFQ\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:acf\/button {\"id\":\"block_67b36f118fcac\",\"name\":\"acf\/button\",\"data\":{\"title\":\"READ HERE\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"https:\/\/link.springer.com\/chapter\/10.1007\/978-3-031-63772-8_5\",\"_url\":\"field_61d4039bc2f0b\",\"button_style\":\"primary\",\"_button_style\":\"field_63872d045d0f0\",\"target\":\"_self\",\"_target\":\"field_63872c705d0ef\",\"button_extra_classes\":\"\",\"_button_extra_classes\":\"field_642beab6a97de\"},\"align\":\"\",\"mode\":\"edit\"} \/-->","post_title":"Investigation of Energy-Efficient AI Model Architectures and Compression Techniques for \u201cGreen\u201d Fetal Brain Segmentation","post_excerpt":"2024","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"investigation-of-energy-efficient-ai-model-architectures-and-compression-techniques-for-green-fetal-brain-segmentation","to_ping":"","pinged":"","post_modified":"2025-02-17 18:30:06","post_modified_gmt":"2025-02-17 17:30:06","post_content_filtered":"","post_parent":0,"guid":"https:\/\/sano.science\/?post_type=research&p=21526","menu_order":0,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":15010,"post_author":"5","post_date":"2024-01-18 09:31:55","post_date_gmt":"2024-01-18 08:31:55","post_content":"<!-- wp:heading {\"epAnimationGeneratedClass\":\"edplus_anim-KEeM7k\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h2 class=\"wp-block-heading eplus-wrapper\">Monika Pytlarz, Kamil Wojnicki, Paulina Pilanc, Bo\u017cena Kami\u0144ska, Alessandro Crimi<\/h2>\n<!-- \/wp:heading -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-GXNp11\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-zCE5mp\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">Gliomas are primary brain tumors that arise from neural stem cells or glial precursors. Diagnosis of glioma is based on histological evaluation of pathological cell features and some molecular markers. Gliomas are infiltrated by myeloid cells that accumulate preferentially in malignant tumors and their abundance inversely correlates with survival, which is of interest for cancer immunotherapies. To avoid time-consuming and laborious manual examination of the images, a deep learning approach for automatic multiclass classification of tumor grades was proposed. Moreover, the challenge of the study was given by small sample size of human leukocyte antigen tissue microarrays dataset (HLA-TMA)-total 204 images from 5 classes-and imbalanced data distribution. This has been addressed by images augmentation of the underrepresented classes, as already shown in a similar study about predicting mutations from glioma biopsies. 1 For this glioma multiclass classification task, the architecture of residual neural network has been adapted. The best model produced an accuracy score of 0.7727, and the mean accuracy of the cross-validation iterations was the value of 0.7248 on the validation set. This promising approach can be used as an additional diagnostic tool to improve assessment during intra-operative examination or sub-typing tissues for treatment selection, despite the challenges presented by the difficult dataset.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-GXNp11\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:acf\/button {\"id\":\"block_65a8e1e78d376\",\"name\":\"acf\/button\",\"data\":{\"title\":\"READ HERE\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"https:\/\/www.researchgate.net\/publication\/369547664_Automated_Glioma_Multiclass_Tumor_Classification\",\"_url\":\"field_61d4039bc2f0b\",\"button_style\":\"primary\",\"_button_style\":\"field_63872d045d0f0\",\"target\":\"_blank\",\"_target\":\"field_63872c705d0ef\",\"button_extra_classes\":\"\",\"_button_extra_classes\":\"field_642beab6a97de\"},\"align\":\"\",\"mode\":\"edit\"} \/-->","post_title":"Automated glioma multiclass tumor classification","post_excerpt":"In: SPIE Medical Imaging 2023: Digital and Computational Pathology, 2023.","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"automated-glioma-multiclass-tumor-classification","to_ping":"","pinged":"","post_modified":"2024-01-18 09:31:55","post_modified_gmt":"2024-01-18 08:31:55","post_content_filtered":"","post_parent":0,"guid":"https:\/\/sano.science\/?post_type=research&p=15010","menu_order":9,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":15033,"post_author":"5","post_date":"2024-01-18 10:19:04","post_date_gmt":"2024-01-18 09:19:04","post_content":"<!-- wp:heading {\"epAnimationGeneratedClass\":\"edplus_anim-7d2931\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h2 class=\"wp-block-heading eplus-wrapper\">Monika Pytlarz, Adrian Onicas, Alessandro Crimi<\/h2>\n<!-- \/wp:heading -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-3TibNG\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-AvMVpe\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">Cross-modal augmentation of Magnetic Resonance Imaging (MRI) and microscopic imaging based on the same tissue samples is promising because it can allow histopathological analysis in the absence of an underlying invasive biopsy procedure. Here, we tested a method for generating microscopic histological images from MRI scans of the corpus callosum using conditional generative adversarial network (cGAN) architecture. To our knowledge, this is the first multimodal translation of the brain MRI to histological volumetric representation of the same sample. The technique was assessed by training paired image translation models taking sets of images from MRI scans and microscopy. The use of cGAN for this purpose is challenging because microscopy images are large in size and typically have low sample availability. The current work demonstrates that the framework reliably synthesizes histology images from MRI scans of corpus callosum, emphasizing the network's ability to train on high resolution histologies paired with relatively lower-resolution MRI scans. With the ultimate goal of avoiding biopsies, the proposed tool can be used for educational purposes.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-Hqcac4\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:acf\/button {\"id\":\"block_65df5d5ef1033\",\"name\":\"acf\/button\",\"data\":{\"title\":\"READ MORE\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"https:\/\/arxiv.org\/abs\/2310.10414\",\"_url\":\"field_61d4039bc2f0b\",\"button_style\":\"primary\",\"_button_style\":\"field_63872d045d0f0\",\"target\":\"_blank\",\"_target\":\"field_63872c705d0ef\",\"button_extra_classes\":\"\",\"_button_extra_classes\":\"field_642beab6a97de\"},\"align\":\"\",\"mode\":\"edit\"} \/-->","post_title":"Style transfer between microscopy and magnetic resonance imaging via generative adversarial network in small sample size settings","post_excerpt":"In: IEEE International Conference on Image Processing (ICIP), Kuala Lumpur, Malaysia, 2023.","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"style-transfer-between-microscopy-and-magnetic-resonance-imaging-via-generative-adversarial-network-in-small-sample-size-settings","to_ping":"","pinged":"","post_modified":"2024-02-28 17:23:04","post_modified_gmt":"2024-02-28 16:23:04","post_content_filtered":"","post_parent":0,"guid":"https:\/\/sano.science\/?post_type=research&p=15033","menu_order":13,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":15660,"post_author":"5","post_date":"2024-03-11 12:46:21","post_date_gmt":"2024-03-11 11:46:21","post_content":"<!-- wp:heading {\"epAnimationGeneratedClass\":\"edplus_anim-sB7KXZ\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h2 class=\"wp-block-heading eplus-wrapper\">M. Pytlarz,\u00a0K. Wojnicki,\u00a0P. Pilanc,\u00a0B. Kaminska,\u00a0A. Crimi<\/h2>\n<!-- \/wp:heading -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-gb0zOc\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-RWKNw1\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">Gliomas are primary brain tumors that arise from neural stem cells, or glial precursors. Diagnosis of glioma is based on histological evaluation of pathological cell features and molecular markers. Gliomas are infiltrated by myeloid cells that accumulate preferentially in malignant tumors, and their abundance inversely correlates with survival, which is of interest for cancer immunotherapies. To avoid time-consuming and laborious manual examination of images, a deep learning approach for automatic multiclass classification of tumor grades was proposed. As an alternative way of investigating characteristics of brain tumor grades, we implemented a protocol for learning, discovering, and quantifying tumor microenvironment elements on our glioma dataset. Using only single-stained biopsies we derived characteristic differentiating tumor microenvironment phenotypic neighborhoods. The study was complicated by the small size of the available human leukocyte antigen stained on glioma tissue microarray dataset \u2014 206 images of 5 classes \u2014 as well as imbalanced data distribution. This challenge was addressed by image augmentation for underrepresented classes. In practice, we considered two scenarios, a whole slide supervised learning classification, and an unsupervised cell-to-cell analysis looking for patterns of the microenvironment. In the supervised learning investigation, we evaluated 6 distinct model architectures. Experiments revealed that a DenseNet121 architecture surpasses the baseline\u2019s accuracy by a significant margin of 9% for the test set, achieving a score of 69%, increasing accuracy in discerning challenging WHO grade 2 and 3 cases. All experiments have been carried out in a cross-validation manner. The tumor microenvironment analysis suggested an important role for myeloid cells and their accumulation in the context of characterizing glioma grades. Those promising approaches can be used as an additional diagnostic tool to improve assessment during intraoperative examination or subtyping tissues for treatment selection, potentially easing the workflow of pathologists and oncologists.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-avafED\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:acf\/button {\"id\":\"block_65eeeef21ef7c\",\"name\":\"acf\/button\",\"data\":{\"title\":\"READ HERE\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"https:\/\/link.springer.com\/article\/10.1007\/s10278-024-01008-x\",\"_url\":\"field_61d4039bc2f0b\",\"button_style\":\"primary\",\"_button_style\":\"field_63872d045d0f0\",\"target\":\"_blank\",\"_target\":\"field_63872c705d0ef\",\"button_extra_classes\":\"\",\"_button_extra_classes\":\"field_642beab6a97de\"},\"align\":\"\",\"mode\":\"edit\"} \/-->","post_title":"Deep Learning Glioma Grading with the Tumor Microenvironment Analysis Protocol for Comprehensive Learning, Discovering, and Quantifying Microenvironmental Features","post_excerpt":"In: Journal of Imaging Informatics in Medicine, 2024.","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"deep-learning-glioma-grading-with-the-tumor-microenvironment-analysis-protocol-for-comprehensive-learning-discovering-and-quantifying-microenvironmental-features","to_ping":"","pinged":"","post_modified":"2024-03-11 12:47:13","post_modified_gmt":"2024-03-11 11:47:13","post_content_filtered":"","post_parent":0,"guid":"https:\/\/sano.science\/?post_type=research&p=15660","menu_order":0,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"}]},"_links":{"self":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/people\/12038","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/people"}],"about":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/types\/people"}],"version-history":[{"count":13,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/people\/12038\/revisions"}],"predecessor-version":[{"id":24913,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/people\/12038\/revisions\/24913"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/media\/28733"}],"wp:attachment":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/media?parent=12038"}],"wp:term":[{"taxonomy":"people_teams","embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/people_teams?post=12038"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}